CN117051111A - 生物标志物组合在制备预测肺癌的试剂盒中的应用 - Google Patents
生物标志物组合在制备预测肺癌的试剂盒中的应用 Download PDFInfo
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Abstract
本发明公开了生物标志物组合在制备预测肺癌的试剂盒中的应用。具体地,公开了一种生物标志物组合在制备预测和/或诊断肺癌的试剂盒中的应用;所述生物标志物组合由111种生物标志物组成,所述试剂盒包含检测所述生物标志物组合中生物标志物的表达水平的试剂。本发明的生物标志物组合在预测肺癌风险中具有高灵敏度和高特异性的优点,为预测肺癌发生发展提供有利的技术支持,具有广泛的科研价值并为早期临床诊断、干预治疗等提供了巨大的便利。
Description
技术领域
本发明属于生物医药技术和诊断领域,具体涉及生物标志物组合在制备预测肺癌的试剂盒中的应用。
背景技术
在全球范围内,肺癌是最常见也是最致命的恶性肿瘤。据估计,每年有180万的肺癌新增确诊病例。肺癌的死亡率很高,占所有因癌症导致死亡病例的20%。早期肺癌患者5年生存率超过80%,而晚期肺癌的5年生存率仅8-10%,早诊断早治疗对肺癌患者治愈至关重要。
因此,精准灵敏的早期筛查手段是十分有必要的。传统癌症肿瘤筛查手段包括影像学检查,病理学检查,放射及免疫学检查等。体检中各项血液检查指标,X光等都是常用的筛查肺癌的方法。然而这些手段都存在假阳性率过高,发现时间较晚等缺点。
因此,急需一种高灵敏度高准确率的诊断方法来实现早期癌症筛查。
蛋白质组学在揭示肿瘤发生的复杂分子事件,如肿瘤发生、侵袭、转移和对治疗耐受上起了重大作用。蛋白质组学肿瘤诊断具有灵敏度高、特异性强、背景机理明确的优点,近年来被越来越多地运用于肿瘤检测。而且,这些肿瘤标志物的研究往往是基于一定量的实验数据,所涉及的癌症种类和样本量都相对有限。近年来,随着蛋白质组不断发展,体液蛋白质组大数据不断增加。因此,通过收集体液蛋白质组数据,利用大数据分析方法,找到一种适用范围广、准确率高的肿瘤风险模型,有助于实现早期诊断,对患者进行早诊早治疗具有重要的临床意义。
发明内容
本发明所要解决的技术问题是现有技术缺少能够早期准确预测肺癌风险的生物标志物,提供了生物标志物组合在制备预测肺癌的试剂盒中的应用。本发明的生物标志物组合在预测早期肺癌风险中具有高灵敏度和高特异性的优点,为预测肺癌发生发展提供有利的技术支持,具有广泛的科研价值并为早期临床诊断、干预治疗等提供了巨大的便利。
本发明通过以下技术方案解决上述技术问题。
本发明的第一方面提供一种生物标志物组合在制备预测和/或诊断肺癌的试剂盒中的应用;
其中,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成。
本发明的第二方面提供一种用于检测生物标志物组合的试剂,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成。
本发明一些实施方案中,所述试剂用于检测所述生物标志物组合的表达水平;所述表达水平为蛋白表达水平和/或mRNA转录水平。
本发明一些较佳实施方案中,所述试剂为与所述生物标志物特异性结合,或者与编码所述生物标志物的核酸特异性杂交的生物分子试剂。
本发明一些实施方案中,所述生物分子试剂选自引物、探针和抗体。
本发明一些实施方案中,所述试剂为用于基因组、转录组和/或蛋白质组测序的试剂。
本发明的第三方面提供一种用于检测生物标志物组合的试剂在制备预测和/或诊断肺癌的试剂盒中的应用;
其中,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成。
本发明一些实施方案中,所述试剂如第二方面所述。
本发明的第四方面提供一种生物标志物组合,所述组合所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成。
本发明的第五方面提供一种试剂盒,所述试剂盒包含如第二方面所述的试剂和如第四方面所述的生物标志物组合。
本发明的第六方面提供一种非诊断目的的检测肺癌的方法,所述方法包括检测待测样本中的生物标志物组合的表达水平;
其中,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成;
所述表达水平为蛋白表达水平和/或mRNA转录水平。
本发明中,所述“非诊断目的”是指出于科学研究、病理数据统计的目的,适用场景包括验证动物模型是否成功构建、体外药效实验、肿瘤的流行病学统计等。
本发明的第七方面提供一种肺癌风险的预测系统,所述预测系统包括检测模块和分析判断模块;所述检测模块检测待测样本中的生物标志物组合的表达水平,并将表达水平数据传输至所述分析判断模块;所述分析判断模块通过Firmiana软件处理所述表达水平数据,预设为基于广义线性回归模型的机器学习算法,构建预测模型,分别预测样本患肺癌的概率和不患肺癌的概率,判断所述表达水平数据是否符合预设的判断条件,以预测样本患肺癌的风险,并输出预测结果;所述判断条件为患肺癌的概率大于或等于不患肺癌的概率;
当所述表达水平数据满足所述判断条件时,输出预测结果为“具有肺癌风险”;当所述表达水平数据不满足所述判断条件时,即患肺癌的概率小于不患肺癌的概率,输出预测结果为“不具有肺癌风险”;
其中,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成;
所述表达水平为蛋白表达水平和/或mRNA转录水平。
本发明一些实施方案中,所述预测系统用于在所述接收或输入完成后,通过Firmiana软件处理所述表达水平数据,预设为基于广义线性回归模型的机器学习算法,构建预测系统。
在发明一较佳实施方案中,所述广义线性回归模型的参数为:采用向后回归的方法筛选标志物,并利用R包Caret的train功能进行模型训练和predict函数进行预测。优选地,所述广义线性回归模型的R包包括:predict.model = train(formula, data= train_data, method = "glm", family='binomial') (formula:模型公式,输入的分子组合;train_data:训练集);预测代码:predict( predict.model, test_data)(predict.model:训练集得到的预测模型,test_data:内部或者外部验证集)。
本发明一些实施方案中,所述待测样本为血浆样本。
本发明一些实施方案中,所述预测系统还包括数据收集模块,所述数据收集模块用于收集待测样本中所述生物标志物组合的表达水平数据。
本发明一些实施方案中,所述预测系统为预测肺癌早期的系统。
本发明一些实施方案中,所述分析判断模块中,训练集参数设置为80%,验证集参数设置为20%。
本发明第八方面提供一种计算机可读存储介质,其存储有计算机程序,所述计算机程序被处理器执行时,可实现如本发明第七方面所述的预测系统的功能,或实现如本发明第六方面所述的方法的步骤。
本发明第九方面提供一种电子设备,其包括存储器和处理器,所述存储器存储有计算机程序,所述处理器用于执行所述计算机程序以实现如本发明第七方面所述的预测系统的功能,或实现如本发明第六方面所述的方法的步骤。
本发明通过用于肺癌筛查的体液蛋白质分子,建立肿瘤风险模型,有助于实现肺癌的早期诊断。
本发明通过筛选体液蛋白质组,获得了一组能够预测肺癌风险的生物标志物,筛选方法包括如下步骤:
(1) 收集健康人和肺癌患者的体液样本;
(2) 健康人和肺癌患者体液样本蛋白质制备;
(3) 检测健康人和肺癌患者体液样品中的蛋白质分子表达水平;
(4) 找到肿瘤患者体液特异性高表达的蛋白质组分子,并构建分类器进行区分。
在符合本领域常识的基础上,上述各优选条件,可任意组合,即得本发明各较佳实例。
本发明所用试剂和原料均市售可得。
本发明的积极进步效果在于:
本发明提供的上述蛋白质分子标志物通过实验发现在肿瘤患者和健康人的体液样本中的表达水平存在显著变化,因此本发明中提供的体液蛋白质分子标志物可以作为肿瘤患者的风险预估与检测,具有高灵敏度和高特异性的优点,为预测肺癌发生发展提供有利的技术支持。
基于健康人和肺癌患者体液样本的蛋白质分子标志物研制相应的预测装置,具有广泛的科研价值并为早期临床诊断、干预治疗等提供了巨大的便利。
附图说明
图1为ROC曲线下面积示意图。
图2为标志物组合在验证集中的预测结果,包括预测准确率,灵敏度和特异性结果。
图3为预测肺癌风险的系统的结构示意图。
图4为电子设备的结构示意图。
具体实施方式
下面通过实施例的方式进一步说明本发明,但并不因此将本发明限制在所述的实施例范围之中。下列实施例中未注明具体条件的实验方法,按照常规方法和条件,或按照商品说明书选择。
“生物标志物”,指可以标记系统、器官、组织、细胞及亚细胞结构或功能的改变或可能发生改变的生化指标,可用于疾病诊断、判断疾病分期或评价新药新疗法在目标人群中的安全性和有效性。
实施例中未注明具体技术或条件者,按照本领域内的文献所描述的技术或条件,或者按照产品说明书进行。所用的试剂或仪器未注明生产厂商者,均为可通过正规渠道商购获得的常规产品。
实施例中包括200例正常人群及232例肺癌病人血浆样本。本研究的设计和实施已通过伦理批准和监督,已获得所有患者的书面知情同意。
实施例1 预测肺癌风险的生物标志物的组合的筛选和验证
1.1 分离血浆
采集全血样品,于EDTA抗凝管中,颠倒混匀后,使用4 ℃低温离心机,1,600 × g离心10 min,离心后收集上清(血浆)至新的EP管中,16,000 × g离心10 min去除细胞碎片,将血浆分装至离心管中,-80℃冻存备用。
1.2 血浆样本前处理
向2 μL血浆样本中加入浓度为100uL 50mM的碳酸氢铵,涡旋混匀1min,将样品95℃加热孵育4 min使蛋白质热变性,冷却至室温后,向体系中加入2 μg胰蛋白质酶(Trypsin),于37℃ 下振荡18 h,然后向体系中加入10 μL氨水停止酶解。将酶解后肽段样本进行脱盐处理,抽干,冻存于-80℃直至质谱检测。
1.3 ASD血浆样本的质谱检测
用Orbitrap Fusion Lumos三合一高分辨质谱系统(Thermo Fisher Scientific,Rockford,USA)串联高效液相色谱系统(EASY-nLC 1200,Thermo Fisher)进行检测,并得到该肽样品对应的全蛋白质的质谱数据。具体操作为:
采用纳流液相色谱,色谱柱为自制C18色谱柱(150 μm ID×8 cm,1.9 μm/120Å填料)。柱温箱温度60℃。将干粉状肽使用上样缓冲液(0.1%甲酸的水溶液)复溶,上样后经色谱柱分离,以600 nL / min的线性6–30%流动相B(ACN和0.1%甲酸)洗脱,利用10 min液相梯度结合数据非依赖性获取(Data Independent Acquisition,DIA)的质谱检测手段。DIA质谱检测参数设置如下:离子模式为正离子;一级质谱分辨率30K,最大注入时间为20 ms,AGC Target为3e6,扫描范围为300-1400 m/z;二级扫描分辨率15K,获取30个可变隔离窗口,碰撞能量27%。液相色谱串联质谱系统使用Xcalibur软件控制进行数据采集。
1.4 数据分析
所有数据均使用Firmiana(V1.0)进行处理。Firmiana是一个基于Galaxy系统的工作流,由用户登录界面、原数据、识别与量化、数据分析和知识挖掘等多个功能模块组成。DIA数据使用DIANN(v12.1)对UniProt人蛋白质数据库(于2019.12.17更新,20406个条目)进行搜索。母离子的质量差为20 ppm,子离子的质量差为50 mmu。最多允许两个漏切位点。搜索引擎将半胱氨酸氨基甲酰甲基化设置为固定修饰,将甲硫氨酸的N-乙酰化和氧化设置为可变修饰。母离子电荷范围设为+ 2,+ 3和+4。错误发现率(False Discovery Rate,FDR)设为1%。使用SpectraST软件将DIA数据的结果合并到参考库中。总共327个库用作参考库。
将所鉴定的肽段定量结果记为所有参考谱库中色谱碎片离子峰面积的平均值。使用无标签的基于强度的绝对定量(Intensity Based Absolute Auantification,iBAQ)方法进行蛋白质定量。我们计算了峰面积值作为相应蛋白质的一部分。总分数(FOT)用于表示样品中特定蛋白质的标准化丰度。FOT定义为蛋白质的iBAQ除以样品中所有已鉴定蛋白质的总iBAQ。选择具有至少一条专属肽段(unique peptide)且1% FDR的蛋白质。
本实施例选择的Firmiana预设为基于广义线性回归模型的机器学习算法,构建预测模型,分别预测样本患肺癌的概率和不患肺癌的概率。
所述广义线性回归模型的参数为:采用向后回归的方法筛选标志物,并利用R包Caret的train功能进行模型训练和predict函数进行预测。优选地,所述广义线性回归模型的R包包括:predict.model = train(formula, data= train_data, method = "glm",family='binomial') (formula:模型公式,输入的分子组合;train_data:训练集);预测代码:predict( predict.model, test_data)(predict.model:训练集得到的预测模型,test_data:内部或者外部验证集)。
输入模型的样本蛋白表达矩阵如下表1:
表1 样本中蛋白中表达矩阵
实验发现部分蛋白质在肿瘤患者和健康人的体液样本中的表达水平存在显著变化,对肺癌患者的血浆样本中的111种蛋白质分子标志物(ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN。)的相对表达水平绘制ROC曲线(ReceiverOperating Curve)计算AUC(Area Under the ROC Curve),其中训练集包括174例阳性病例,150例阴性病例,AUC=0.99,诊断灵敏度100.00% ,特异性100.00%(见图1),内部验证集扩剩余58例阳性病例,50例阴性病例,诊断灵敏度95% ,特异性97%(见图2)。分析方法参见Karimollah Hajian-Tilaki, Receiver Operating Characteristic (ROC) CurveAnalysis for Medical Diagnostic Test Evaluation, Caspian J Intern Med2013; 4(2): 627-635。对于未知样本,将上述生物标志物的表达水平代入模型,得到该样本的肺癌风险预测并输出结果,当患肺癌的概率大于或等于不患肺癌的概率,输出预测结果为“具有肺癌风险”;当患肺癌的概率小于不肺癌的概率,输出预测结果为“不具有肺癌风险”。
由上述结果可知,将肿瘤患者血浆中的111种蛋白质分子标志物(ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN。)联用,可用于预测肿瘤风险。
实施例2 预测肺癌风险的系统
预测肺癌风险的系统61:数据处理模块52和判断并输出模块53,还包括数据收集模块51(图3)。
数据收集模块51用于收集患者肺癌组织样本中所述生物标志物组合的表达水平数据,并将其传输给数据处理模块。
数据处理模块52用于将接收或输入生物标志物组合的表达水平数据按如实施例1所述的数据分析方法进行分析,得到计算结果。其中,所述生物标志物组合的表达水平数据可通过数据收集模块51进行收集,亦可从其他来源获取所述生物标志物组合的表达水平数据。
判断并输出模块53用于判断所述的计算结果是否符合预设的判断条件,即患肺癌风险概率大于或等于不患肺癌风险预测概率,以预测肺癌风险,并输出预测结果;其中,在所述判断并输出模块中,当所述表达水平数据满足所述判断条件患肺癌风险概率大于或等于不患肺癌风险预测概率时,输出预测结果为“具有肺癌风险”;当所述表达水平数据不满足所述判断条件患肺癌风险概率小于不患肺癌风险预测概率时,输出预测结果为“不具有肺癌风险”。
实施例3 电子设备
本实施例提供了一种电子设备,电子设备可以通过计算设备的形式表现(例如可以为服务器设备),包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,其中处理器执行计算机程序时可以实现本发明实施例1中预测肺癌风险的方法。
图4示出了本实施例的硬件结构示意图,电子设备9具体包括:
至少一个处理器91、至少一个存储器92以及用于连接不同系统组件(包括处理器91和存储器92)的总线93,其中:
总线93包括数据总线、地址总线和控制总线。
存储器92包括易失性存储器,例如随机存取存储器(RAM)921和/或高速缓存存储器922,还可以进一步包括只读存储器(ROM)923。
存储器92还包括具有一组(至少一个)程序模块924的程序,和/或,程序工具925,这样的程序模块924包括但不限于:操作系统、一个或者多个应用程序、其它程序模块以及程序数据,这些示例中的每一个或某种组合中可能包括网络环境的实现。
处理器91通过运行存储在存储器92中的计算机程序,从而执行各种功能应用以及数据处理,例如本发明实施例1的数据分析方法。
电子设备9进一步可以与一个或多个外部设备94(例如键盘、指向设备等)通信。这种通信可以通过输入/输出(I/O)接口95进行。并且,电子设备9还可以通过网络适配器96与一个或者多个网络(例如局域网(LAN),广域网(WAN)和/或公共网络,例如因特网)通信。网络适配器96通过总线93与电子设备9的其它模块通信。应当明白,尽管图中未示出,可以结合电子设备9使用其它硬件和/或软件模块,包括但不限于:微代码、设备驱动器、冗余处理器、外部磁盘驱动阵列、RAID(磁盘阵列)系统、磁带驱动器以及数据备份存储系统等。
应当注意,尽管在上文详细描述中提及了电子设备的若干单元/模块或子单元/模块,但是这种划分仅仅是示例性的并非强制性的。实际上,根据本申请的实施方式,上文描述的两个或更多单元/模块的特征和功能可以在一个单元/模块中具体化。反之,上文描述的一个单元/模块的特征和功能可以进一步划分为由多个单元/模块来具体化。
实施例4 计算机可读存储介质
本发明实施例提供了一种计算机可读存储介质,其上存储有计算机程序,程序被处理器执行时实现本发明实施例1中预测肺癌风险的方法的步骤。
其中,可读存储介质可以采用的更具体可以包括但不限于:便携式盘、硬盘、随机存取存储器、只读存储器、可擦拭可编程只读存储器、光存储器件、磁存储器件或上述的任意合适的组合。
在可能的实施方式中,本发明还可以实现为一种程序产品的形式,其包括程序代码,当所述程序产品在终端设备上运行时,所述程序代码用于使所述终端设备执行实现本发明实施例1中预测肺癌风险的方法的步骤。
其中,可以以一种或多种程序设计语言的任意组合来编写用于执行本发明的程序代码,所述程序代码可以完全地在用户设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户设备上部分在远程设备上执行或完全在远程设备上执行。
最后,上述具体实施方法仅用以说明本发明的技术方案,而非对其限制。
生物标志物名称:(可参考NCBI或genecards数据库)
ABRACL:ABRA C-terminal like,Gene ID: 58527
ACTA1:actin alpha 1,Gene ID: 58
ACTA2:actin alpha 2,Gene ID: 59
ACTB:actin beta,Gene ID: 60
ACTBL2:actin beta like 2,Gene ID: 345651
ACTC1:Actin Alpha Cardiac Muscle 1,Gene ID: 70
ACTG1:Actin Gamma 1,Gene ID: 71
ACTG2:Actin Gamma 2,Gene ID: 72
ACTR3B:actin related protein 3B,Gene ID: 57180
ACTR3C:actin related protein 3C,Gene ID: 653857
ACY1:aminoacylase 1,Gene ID: 95
ALDH5A1:aldehyde dehydrogenase 5 family member A1,Gene ID: 7915
ALDH9A1:aldehyde dehydrogenase 9 family member A1,Gene ID: 223
ALG14:ALG14 UDP-N-acetylglucosaminyltransferase subunit,Gene ID:199857
ARMC8:Armadillo Repeat Containing 8,Gene ID: 25852
ARPC5:actin related protein 2/3 complex subunit 5,Gene ID: 10092
ATP5PF:ATP synthase peripheral stalk subunit F6,Gene ID: 522
BCAS2:BCAS2 pre-mRNA processing factor,Gene ID: 10286
BHMT:betaine--homocysteine S-methyltransferase,Gene ID: 635
BHMT2:betaine--homocysteine S-methyltransferase 2,Gene ID: 23743
BIN2:bridging integrator 2,Gene ID: 51411
CALD1:caldesmon 1,Gene ID: 800
CALM1:calmodulin 1,Gene ID: 801
CALM2:calmodulin 2,Gene ID: 805
CALM3:calmodulin 3,Gene ID: 808
CAT:catalase,Gene ID: 847
CAVIN2:caveolae associated protein 2,Gene ID: 8436
CDC37:cell division cycle 37, HSP90 cochaperone,Gene ID: 11140
CFL1:cofilin 1,Gene ID: 1072
CLIC1:chloride intracellular channel 1,Gene ID: 1192
CORO1C:coronin 1C,Gene ID: 23603
CRP:C-reactive protein,Gene ID: 1401
CSRP1:cysteine and glycine rich protein 1,Gene ID: 1465
DAG1:dystroglycan 1,Gene ID: 1605
DUSP3:dual specificity phosphatase 3,Gene ID: 1845
EIF5A2:eukaryotic translation initiation factor 5A2,Gene ID: 56648
EIF5AL1:eukaryotic translation initiation factor 5A like 1,Gene ID:143244
ENO2:enolase 2,Gene ID: 2026
FHL1:four and a half LIM domains 1,Gene ID: 2273
FKBP1A:FKBP prolyl isomerase 1A,Gene ID: 2280
GAPDH:glyceraldehyde-3-phosphate dehydrogenase,Gene ID: 2597
GDA:guanine deaminase,Gene ID: 9615
GP1BB:glycoprotein Ib platelet subunit beta,Gene ID: 2812
GP6:glycoprotein VI platelet,Gene ID: 51206
GSTO1:glutathione S-transferase omega 1,Gene ID: 9446
H1-1:H1.1 linker histone, cluster member,Gene ID: 3024
H1-10:H1.10 linker histone,Gene ID: 8971
HBE1:hemoglobin subunit epsilon 1,Gene ID: 3046
HOOK1:hook microtubule tethering protein 1,Gene ID: 51361
HPRT1:hypoxanthine phosphoribosyltransferase 1,Gene ID: 3251
HSP90AA5P:heat shock protein 90 alpha family class A member 5,pseudogene,Gene ID: 730211
HSPA6:heat shock protein family A (Hsp70) member 6,Gene ID: 3310
HSPA7:heat shock protein family A (Hsp70) member 7 (pseudogene),GeneID: 3311
ITGA2B:integrin subunit alpha 2b,Gene ID: 3674
LGALSL:galectin like,Gene ID: 29094
MAP1A:microtubule associated protein 1A,Gene ID: 4130
MAPK8IP3:mitogen-activated protein kinase 8 interacting protein 3,Gene ID: 23162
MCU:mitochondrial calcium uniporter,Gene ID: 90550
MIF:macrophage migration inhibitory factor,Gene ID: 4282
MTPN:myotrophin,Gene ID: 136319
MYL1:myosin light chain 1,Gene ID: 4632
MYL3:myosin light chain 3,Gene ID: 4634
MYL6B:myosin light chain 6B,Gene ID: 140465
NAXD:NAD(P)HX dehydratase,Gene ID: 55739
NCALD:neurocalcin delta,Gene ID: 83988
OGT:O-linked N-acetylglucosamine (GlcNAc) transferase,Gene ID: 8473
PARVB:parvin beta,Gene ID: 29780
PCBP1:poly(rC) binding protein 1,Gene ID: 5093
PCBP3:poly(rC) binding protein 3,Gene ID: 54039
PDLIM3:PDZ and LIM domain 3,Gene ID: 27295
PFN1:profilin 1,Gene ID: 5216
PHGDH:phosphoglycerate dehydrogenase,Gene ID: 26227
PKM:pyruvate kinase M1/2,Gene ID: 5315
PLAA:phospholipase A2 activating protein,Gene ID: 9373
PLBD2:phospholipase B domain containing 2,Gene ID: 196463
PLCB2:phospholipase C beta 2,Gene ID: 5330
PMVK:phosphomevalonate kinase,Gene ID: 10654
POTEE:POTE ankyrin domain family member E,Gene ID: 445582
POTEF:POTE ankyrin domain family member F,Gene ID: 728378
POTEI:POTE ankyrin domain family member I,Gene ID: 653269
POTEJ:POTE ankyrin domain family member J,Gene ID: 653781
POTEKP:POTE ankyrin domain family member K, pseudogene,Gene ID:440915
PRAF2:PRA1 domain family member 2,Gene ID: 11230
PROS1:protein S,Gene ID: 5627
PSMC6:proteasome 26S subunit, ATPase 6,Gene ID: 5706
PXDN:peroxidasin,Gene ID: 7837
RAB1B:RAB1B, member RAS oncogene family,Gene ID: 81876
RAB1C:RAB1C, member RAS oncogene family pseudogene,Gene ID: 441400
RAB39A:RAB39A, member RAS oncogene family,Gene ID: 54734
RDH10:retinol dehydrogenase 10,Gene ID: 157506
RDX:radixin,Gene ID: 5962
RPAP1:RNA polymerase II associated protein 1,Gene ID: 26015
RUVBL1:RuvB like AAA ATPase 1,Gene ID: 8607
S100A4:S100 calcium binding protein A4,Gene ID: 6275
S100A9:S100 calcium binding protein A9,Gene ID: 6280
SAA1:serum amyloid A1,Gene ID: 6288
SCRN1:secernin 1,Gene ID: 9805
SEC16A:SEC16 homolog A,Gene ID: 9919
SEC24C:SEC24 homolog C,Gene ID: 9632
SFTPB:surfactant protein B,Gene ID: 6439
SH3BGRL3:SH3 domain binding glutamate rich protein like 3,Gene ID:83442
SKP1:S-phase kinase associated protein 1,Gene ID: 6500
SMC1A:structural maintenance of chromosomes 1A,Gene ID: 8243
SMC2:structural maintenance of chromosomes 2,Gene ID: 10592
SNCA:synuclein alpha,Gene ID: 6622
SNCB:synuclein beta,Gene ID: 6620
THBS1:thrombospondin 1,Gene ID: 7057
TMPRSS13:transmembrane serine protease 13,Gene ID: 84000
TMSB4X:thymosin beta 4 X-linked,Gene ID: 7114
TPM4:tropomyosin 4,Gene ID: 7171
TXN:thioredoxin,Gene ID: 7295
Claims (15)
1.一种生物标志物组合在制备预测和/或诊断肺癌的试剂盒中的应用;
其中,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成。
2.一种用于检测生物标志物组合的试剂,其特征在于,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成。
3.如权利要求2所述的试剂,其特征在于,所述试剂用于检测所述生物标志物组合的表达水平;
所述表达水平为蛋白表达水平和/或mRNA转录水平。
4.如权利要求3所述的试剂,其特征在于,所述试剂为与所述生物标志物特异性结合,或者与编码所述生物标志物的核酸特异性杂交的生物分子试剂。
5.如权利要求4所述的试剂,其特征在于,所述生物分子试剂选自引物、探针和抗体。
6.如权利要求3所述的试剂,其特征在于,所述试剂为用于基因组、转录组和/或蛋白质组测序的试剂。
7.一种用于检测生物标志物组合的试剂在制备预测和/或诊断肺癌的试剂盒中的应用;
其中,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成。
8.如权利要求7所述的应用,其特征在于,所述试剂如权利要求3~6任一项所述。
9.一种试剂盒,其特征在于,所述试剂盒包含如权利要求2~6任一项所述的试剂。
10.一种非诊断目的的检测肺癌的方法,其特征在于,所述方法包括检测待测样本中的生物标志物组合的表达水平;
其中,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成;
所述表达水平为蛋白表达水平和/或mRNA转录水平。
11.一种肺癌风险的预测系统,其特征在于,所述预测系统包括检测模块和分析判断模块;所述检测模块检测待测样本中的生物标志物组合的表达水平,并将表达水平数据传输至所述分析判断模块;所述分析判断模块通过Firmiana软件处理所述表达水平数据,预设为基于广义线性回归模型的机器学习算法,构建预测模型,分别预测样本患肺癌的概率和不患肺癌的概率,判断所述表达水平数据是否符合预设的判断条件,以预测样本患肺癌的风险,并输出预测结果;所述判断条件为患肺癌的概率大于或等于不患肺癌的概率;
当所述表达水平数据满足所述判断条件时,输出预测结果为具有肺癌风险;当所述表达水平数据不满足所述判断条件时,即患肺癌的概率小于不患肺癌的概率,输出预测结果为不具有肺癌风险;
其中,所述生物标志物组合由ABRACL、ACTA1、ACTA2、ACTB、ACTBL2、ACTC1、ACTG1、ACTG2、ACTR3B、ACTR3C、ACY1、ALDH5A1、ALDH9A1、ALG14、ARMC8、ARPC5、ATP5PF、BCAS2、BHMT、BHMT2、BIN2、CALD1、CALM1、CALM2、CALM3、CAT、CAVIN2、CDC37、CFL1、CLIC1、CORO1C、CRP、CSRP1、DAG1、DUSP3、EIF5A2、EIF5AL1、ENO2、FHL1、FKBP1A、GAPDH、GDA、GP1BB、GP6、GSTO1、H1-1、H1-10、HBE1、HOOK1、HPRT1、HSP90AA5P、HSPA6、HSPA7、ITGA2B、LGALSL、MAP1A、MAPK8IP3、MCU、MIF、MTPN、MYL1、MYL3、MYL6B、NAXD、NCALD、OGT、PARVB、PCBP1、PCBP3、PDLIM3、PFN1、PHGDH、PKM、PLAA、PLBD2、PLCB2、PMVK、POTEE、POTEF、POTEI、POTEJ、POTEKP、PRAF2、PROS1、PSMC6、PXDN、RAB1B、RAB1C、RAB39A、RDH10、RDX、RPAP1、RUVBL1、S100A4、S100A9、SAA1、SCRN1、SEC16A、SEC24C、SFTPB、SH3BGRL3、SKP1、SMC1A、SMC2、SNCA、SNCB、THBS1、TMPRSS13、TMSB4X、TPM4和TXN组成;
所述表达水平为蛋白表达水平和/或mRNA转录水平。
12.如权利要求11所述的预测系统,其特征在于,所述待测样本为人的血浆样本。
13.如权利要求11或12所述的预测系统,其特征在于,所述预测系统还包括数据收集模块,所述数据收集模块用于收集待测样本中所述生物标志物组合的表达水平数据。
14.一种计算机可读存储介质,其存储有计算机程序,其特征在于,所述计算机程序被处理器执行时,可实现如权利要求11-13任一项所述的预测系统的功能,或实现如权利要求10所述的方法的步骤。
15.一种电子设备,其包括存储器和处理器,所述存储器存储有计算机程序,其特征在于,所述处理器用于执行所述计算机程序以实现如权利要求11-12任一项所述的预测系统的功能,或实现如权利要求10所述的方法的步骤。
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